A company stores CSV files in an Amazon S3 bucket. A data engineer needs to process the data in the CSV files and store the processed data in a new S3 bucket. The process needs to rename a column…

Question

Options

• AUse AWS Glue Python jobs to read and transform the CSV files.
• BUse an AWS Glue custom crawler to read and transform the CSV files.
• CUse an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files.
• DUse AWS Glue DataBrew recipes to read and transform the CSV files.

• A
  9% (4)
• B
  23% (11)
• C
  13% (6)
• D
  55% (26)

Explanation

AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. Using DataBrew recipes, you can easily perform transformations such as renaming columns, removing specific columns, ignoring certain rows, creating new columns, and filtering data based on column values. This solution requires the least development effort because it provides a no-code/low-code interface for performing these tasks. While AWS Glue Python jobs can handle these transformations, they would require writing custom code, which involves more development effort compared to using DataBrew. AWS Glue crawlers are used for cataloging data and are not suitable for performing complex transformations like ignoring rows, renaming columns, or creating new columns. Using an AWS Glue workflow to build a set of jobs to crawl and transform the CSV files adds unnecessary complexity. You would need to orchestrate multiple jobs and workflows, which requires more setup and development compared to using DataBrew for the transformations.

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